size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9961285
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] -0.8688265 -1.28725334 -0.8976981
[2,] -0.2736044 -0.04643061 -0.1167609
[3,] -0.6198895 -0.45114324 -0.5358437
[4,] -0.8775559 -0.66980976 -0.8312106
[5,] 1.5783380 1.61088368 1.5759730
[6,] -1.2602612 -1.05989152 -0.9714201
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.996 -0.561 0.989 -0.547 -0.552 0.988
y3 0.996 1.000 -0.565 0.990 -0.556 -0.557 0.990
x2 -0.561 -0.565 1.000 -0.587 0.980 0.983 -0.590
y1 0.989 0.990 -0.587 1.000 -0.584 -0.582 0.994
x1 -0.547 -0.556 0.980 -0.584 1.000 0.993 -0.569
x3 -0.552 -0.557 0.983 -0.582 0.993 1.000 -0.570
y2 0.988 0.990 -0.590 0.994 -0.569 -0.570 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
Copyright © 2021 Biomedical Data Sciences (BDS) | LUMC